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Heracles.py

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+# Heracles Strategy: Strongest Son of GodStra
+# ( With just 1 Genome! its a bacteria :D )
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
+#   {
+#       "method": "AgeFilter",
+#       "min_days_listed": 100
+#   },
+# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
+#
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces roi buy --strategy Heracles
+# ######################################################################
+# --- Do not remove these libs ---
+from freqtrade.strategy.parameters import  IntParameter, DecimalParameter
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+# --------------------------------
+# Add your lib to import here
+# import talib.abstract as ta
+import pandas as pd
+import ta
+from ta.utils import dropna
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from functools import reduce
+import numpy as np
+
+
+class Heracles(IStrategy):
+    ########################################## RESULT PASTE PLACE ##########################################
+    # 10/100:     25 trades. 18/4/3 Wins/Draws/Losses. Avg profit   5.92%. Median profit   6.33%. Total profit  0.04888306 BTC (  48.88Σ%). Avg duration 4 days, 6:24:00 min. Objective: -11.42103
+
+    # Buy hyperspace params:
+    buy_params = {
+        "buy_crossed_indicator_shift": 9,
+        "buy_div_max": 0.75,
+        "buy_div_min": 0.16,
+        "buy_indicator_shift": 15,
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+    }
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.598,
+        "644": 0.166,
+        "3269": 0.115,
+        "7289": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.256
+
+    # Optimal timeframe use it in your config
+    timeframe = '4h'
+
+    # Trailing stoploss
+    trailing_stop = True
+    trailing_stop_positive = 0.001
+    trailing_stop_positive_offset = 0.015
+    trailing_only_offset_is_reached = True
+
+    ########################################## END RESULT PASTE PLACE ######################################
+
+    # buy params
+    buy_div_min = DecimalParameter(0, 1, default=0.16, decimals=2, space='buy')
+    buy_div_max = DecimalParameter(0, 1, default=0.75, decimals=2, space='buy')
+    buy_indicator_shift = IntParameter(0, 20, default=16, space='buy')
+    buy_crossed_indicator_shift = IntParameter(0, 20, default=9, space='buy')
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe = dropna(dataframe)
+
+        dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
+            dataframe['high'],
+            dataframe['low'],
+            dataframe['close'],
+            window=20,
+            window_atr=10,
+            fillna=False,
+            original_version=True
+        )
+
+        dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
+            dataframe['high'],
+            dataframe['low'],
+            dataframe['close'],
+            window=10,
+            offset=0,
+            fillna=False
+        )
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Buy strategy Hyperopt will build and use.
+        """
+        conditions = []
+
+        IND = 'volatility_dcp'
+        CRS = 'volatility_kcw'
+        DFIND = dataframe[IND]
+        DFCRS = dataframe[CRS]
+
+        d = DFIND.shift(self.buy_indicator_shift.value).div(
+            DFCRS.shift(self.buy_crossed_indicator_shift.value))
+
+        # print(d.min(), "\t", d.max())
+        conditions.append(
+            d.between(self.buy_div_min.value, self.buy_div_max.value))
+
+        if conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'buy']=1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Sell strategy Hyperopt will build and use.
+        """
+        dataframe.loc[:, 'sell'] = 0
+        return dataframe